Methodology

Our quantitative data infrastructure is the world’s most comprehensive and quality controlled. We study more than 4 million companies across 350+ startup ecosystems, combine data from the three leading venture funding databases, and then remove duplicates and clean with an AI engine, machine learning techniques, and a manual review. Additionally, we work with 50+ countries to power and update the data found in our reports and policy consultancy work.

Key Definitions

Ecosystem: We define a startup ecosystem as a shared pool of resources, generally located within a 62 mile (100 kilometer) radius around a center point in a given region, with a few exceptions based on local reality. Resources typically include policymakers, accelerators, incubators, coworking spaces, educational institutions, and funding groups.

Exit: An exit, in the context of startups, refers to an event in which the founders, investors, or employees of a startup realize a return on their investment by selling their ownership stake in the company. Exits include IPOs, M&A, buyouts, and reverse mergers. We only include the first exit as relevant.

H1/H2: Fiscal periods of half a year, in which January–June is H1 and July–December is H2. Similarly, Q1, Q2, etc. refers to the four fiscal quarters of a year (January–March, April–June, etc.).

Regions: We define global regions based on UN and World Bank definitions and divide all countries into seven regions: Asia, Europe, Latin America, MENA, North America, Oceania, sub-Saharan Africa. For a full list of which ecosystems are included in each region, please see here.

Startup: We define a startup as an innovative or technology-driven company that was founded within the last 10 years and that has technology and/or scalability at the core of its business model. In addition to software, this includes startups active in Deep Tech, such as Robotics, Life Sciences, and more.

Unicorn: A startup that meets our definition, has been valued at more than $1 billion, and has not exited.

Sub-Sector Definitions

Sub-sectors are not mutually exclusive nor comprehensive — some startups are in sub-sectors that we do not consider. In addition, we are aware of a clear tech convergence. Technologies such as AI software are increasingly interrelated, and we would expect a similar convergence over time for other startup sub-sectors.

Advertising Tech (Adtech): Captures different types of analytics and digital tools used in the context of advertising and marketing. Extensive and complex systems are used to direct, convey, or monitor advertising to target audiences of any size and scale.

Advanced Manufacturing & Robotics (AMR): The use of smart technology to improve traditional manufacturing of products and/or processes, and the science and technology of robots, their design, manufacture, and application.

Agriculture Tech (Agtech) & New Food: Agtech captures the use of technology in agriculture, horticulture, and aquaculture with the aim of improving yield, efficiency, and profitability through information monitoring and analysis of weather, pests, and soil and air temperature. New Food includes technologies that can be leveraged to create efficiency and sustainability in designing, producing, choosing, delivering, and consuming food.

Artificial Intelligence, Big Data & Analytics (AI & BD): An area of technology devoted to extracting meaning from large sets of raw data, e.g., often including simulations of intelligent behavior in computers.

Blockchain: A decentralized data storage method secured by cryptography. Companies building their product/architecture on top of this decentralized and encrypted technology are defined as Blockchain companies. Cryptocurrencies are one of many innovations utilizing Blockchain.

Cleantech: Sustainable solutions in the fields of energy, water, transportation, agriculture, and manufacturing that include advanced materials, smart grids, water treatment, efficient energy storage, and distributed energy systems.

Cybersecurity: The body of technologies, processes, and practices designed to protect networks, computers, programs, and data from attack, damage, or unauthorized access.

Edtech: Technology devoted to the development and application of tools (including software, hardware, and processes) intended to redesign traditional products and services in education.

Fintech: Technology that aims to improve existing processes, products, and services in the Financial Services industry (including insurance).

Gaming: The development, marketing, and monetization of video games and gambling machines, as well as associated services.

Life Sciences: Life Sciences is concerned with diagnosing, treating, and managing diseases and conditions. This includes startups in Biotech, Pharma, and Medtech (also referred to as medical devices).

Ecosystem Page Metrics

Ecosystem Value: A measure of economic impact, calculated as the value of exits and startup valuations from H2 2021–2023. Ecosystem Value growth (CAGR) is calculated based on companies founded in the ecosystem in H2 2019–2021 vs. H2 2021–2023.

Total Early-Stage Funding: The total seed and Series A funding in tech startups in H2 2021–2023.

Total VC Funding: The total VC funding (seed, Series A, Series B+) in tech startups in 2019–2023.

Median Series A: The median of Series A rounds in tech startups in the ecosystem in H2 2021–2023.

Median Seed: The median of seed rounds in tech startups in the ecosystem in H2 2021–2023.

Software Engineer Salary: Average software engineer salary informed by data from Glassdoor, Salary.com, and PayScale, as well as local sources when available.

Time to Exit: The average age at the time of exit in the ecosystem in 2019–2023.

For additional definitions, please see the Glossary on our website.

Primary Data Sources

• Startup Genome proprietary data:
  • Interviews of 100+ experts
  • 2017–2023 Startup Ecosystem Survey with more than 10,000 participants per year
    
• Dealroom: global dataset on funding, exits, and locations of startups and investors
• Crunchbase: global dataset on funding, exits, and locations of startups and investors
• PitchBook: private capital market data provider
• Local partners (accelerators, incubators, startup hubs, investors):
  • List of startups
  • List of local exits and funding events
    
• Startup Genome LLC (2017-2023). StartupGenome.com database
• Dealroom.co BV. (2017-2023). Dealroom.co database
• Crunchbase (2017-2023). Crunchbase.com database
• CB Insights (2019-2023). Cbinsights.com database
• Orb Intelligence Inc. (2017-2023). orb-intelligence.com database
• PitchBook (2018-2023), a private capital market data provider

Secondary Data Sources

• Forbes 2000
• GitHub API
• International IP Index
• Meetup.com
• OECD, R&D Spending
• Other sources from Life Sciences Rankings
• Salaries data from Glassdoor, Salary.com, and PayScale
• Shanghai Rankings
• Techboard
• Times Higher Education Rankings
• USPTO
• WIPO
• World Bank

Selected Data Timeframes

• Ecosystem Value: Sum of exits and funding rounds in H2 2021–2023
• Based on long-term research and analysis, we know that it takes around one year for 50% of seed rounds to appear in the major data sources. As such, we use H2 2020 as the most recent period for seed rounds and earlier-stage metrics that are computed to create reliable benchmarks at the ecosystem level.
• For early-stage funding, we take the count of all seed and Series A investments in H2 2020–2022 for seed rounds and H2 2021–2023 for Series A rounds. It takes four to eight weeks for the majority of Series A rounds to appear in our sources.

Ranking Methodology

Global Startup Ecosystem Ranking 2024 (Top 40)

This ranking identifies the Top 40 ecosystems. These ecosystems are more mature than other ecosystems globally, featuring more exits over $50 million and more funding activities.

This ranking is a weighted average of the following factor scores:

• Performance: 30%
• Funding: 25%
• Market Reach: 20%
• Talent & Experience: 20%
• Knowledge: 5%

We calculate an Ecosystem Index Value for each factor, based on the sub-factor and metrics detailed below. The ecosystems scores are multiplied by the above weights to establish the overall rank of each ecosystem. The weights of the factors were determined through correlation analyses and modeling work based on linear regression analyses, using factor indices as independent variables with the performance index as a dependent variable. Finally, adding the actual Performance Index to the ranking formula serves to include the influence of unobserved factors on the performance of an ecosystem.

Ranking Details

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.

• 50% Ecosystem Value
  • Log of sum of all exits and estimated startups valuations during H2 2021–2023 without double counting
    
• 37.5% Exits
  • 80% volume of exits (80% log of number of $50 million+ exits and 20% log of number of $1 billion+ exits) from H2 2021–2023
  • 20% exit growth index (scored from 1 to 10) from 2020–2021 vs. 2022–2023
    
• 12.5% Startup Success
  • 80% growth-stage success (50% ratio of Series C-to-A Startups and 50% from number of unicorns, companies over $B valuation) from H2 2021–2023
  • 20% early-stage success (ratio of Series B-to-A startups) from H2 2021–2023

Funding

Quantifies funding metrics important to the success of early-stage startups.

• 90% Access
  • 90% early-stage funding volume (80% log of count and 20% log of sum of total early-stage funding deals). The time range for seed rounds is H2 2020–2022 and for Series A rounds is H2 2021–2023
  • 10% log of early-stage funding growth from Seed count from H2 2019–2021 and Series A Count from H2 2020–2022 to Seed count from H2 2020–2022 and Series A Count from H2 2021–2023
    
• 10% Quality and Activity
  • 70% volume of investors (50% log of total number of VCs and CVCs in 2022 and 50% log of total number of investors with $100 million+ assets under management in Q1 2022)
  • 10% experience of investors (50% number of investors with above average exit rates and 50% average years of experience of investors)
  • 20% new investors (50% log of total number of new investors, with less than five years of activity) and 50% ratio of active investors
    

Market Reach

Measures early-stage startup access to customers, allowing them to scale and potentially “go-global.”

• 75% Local Reach
  • 45% Scaleup Production
    • 50% ratio of startups with $1 billion+ valuations to GDP from H2 2021–2023
    • 40% ratio of $50 million+ exits to GDP from H2 2021–2023
    • 10% log of ratio of exits over $50 million from H2 2021–2023 to Series A funding in the first from H2 2021- 2023
      
  • 30% Local Market
    • 90% from the log of GDP of the country
    • 10% from tiers of average number of days to commercialization of IP assets
      
• 25% Global Reach
  • 60% ratio of tech startups (formed after 2012) with international secondary offices
  • 20% from the log of tech companies with secondary offices in the ecosystem
  • 20% from the log of international investors

Talent & Experience

Assesses the talent early-stage startups have access to and the degree of startup experience in an ecosystem.

• 37.5% Talent
  • 80% Tech Talent
    • 90% Quality & Access
      • 70% log of count of $50 million+ exits in 2014–2023
      • 10% share of top Github coders to total Github coders (based on the data available in December 2023)
      • 10% log of count of Github coders on github.com with more than 10 followers (based on the data available in December 2023)
      • 10% English Proficiency Score for 2023
        
    • 10% Cost
      • 50% log of software engineer salary — lower is considered better — from Glassdoor, Salary.com, and PayScale for 2023
      • 50% log of funding runway: ratio of median Series A funding rounds for H2 2021–2023 by software engineer salary
        
  • 20% Life Sciences
    • 30% STEM students: log of number of STEM students
    • 60% Life Sciences access
      • 70% log of number of Life Sciences disciplines
      • 30% log of number of institutes with Life Sciences-related disciplines
        
    • 10% Quality
      • 25% average of CNCI score from Shanghai Rankings
      • 25% average of TOP score from Shanghai Rankings
      • 25% average IC score from Shanghai Rankings
      • 25% average PUB score from Shanghai Rankings
• 62.5% Experience
  
  • 80% startup experience in the ecosystem
    • Log of count of funding of Series A in 2014–2023
  • 20% scaling experience in the ecosystem (the cumulative number of significant exits — over $50 million and $1 billion — over 10 years for startups founded in the ecosystem)
    • 60% log of number of $1 billion+ exits in 2014–2023
    • 40% log of number of $50 million+exits in 2014–2023

Knowledge

Measures innovation through research and patent activity.

• 90% patents (the volume, complexity, and potential of all patents created in the ecosystem)
  • 60% log of tier of number of all the patents in the ecosystem in 2012–2022
  • 20% three-year moving average growth of all patents
  • 20% technology potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces
    • 20% complexity of technology class, based on a PageRank algorithm
    • 30% global growth of technology class
    • 50% size of technology class (log of number of global patents in class) in 2012–2021
      
• 10% research
  • H-index, a measure of publication impact. This metric looks at the production of all research at the country level in 1996–2021.

Emerging Ecosystems Ranking

Emerging ecosystems are startup communities at earlier stages of growth. The methodology for ranking the Top 100 Emerging Ecosystems is designed to reflect this, showcasing the ecosystems displaying high potential to become top global performers in the coming years. The factor weights used to rank these ecosystems differ slightly from those used with the top ecosystems to reflect their emerging status and emphasize the factors that have more influence in ecosystems that are just beginning to grow. Less weight is given to the number of exits over $50 million, and startup activity is more focused on early-stage funding than in the Top 40 ecosystems.

The Emerging Ecosystem Ranking is a weighted average of the following factor scores:

• Performance: 30%
• Funding: 32.5%
• Market Reach: 15%
• Talent & Experience: 17.5%
• Knowledge: 5%

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.

• 75% Ecosystem Value
  • Log of sum of all exits and estimated startups valuations during H2 2021–2023 without double counting
    
• 20% Exits
  • 80% volume of exits (80% log of number of $50 million+ exits and 20% log of number of $1 billion+ exits) in H2 2021–2023
  • 20% Exit Growth Index (scored from 1 to 10) for 2020–2021 vs. 2022–2023
    
• 5% Startup Success
  • 80% growth-stage success (50% ratio of Series C-to-A startups and 50% log of unicorns from H2 2021–2023)

Funding

Quantifies funding metrics important to the success of early-stage startups.

• 100% Access
  • 90% early-stage funding volume (80% log of count and 20% log of sum of total early-stage funding deals). The time range for seed rounds is H2 2020–2022 and for Series A rounds is H2 2021–2023
  • 10% log of early-stage funding growth from Seed count from H2 2019–2021 and Series A Count from H2 2020–2022 to Seed count from H2 2020–2022 and Series A Count from H2 2021–2023

Market Reach

Measures early-stage startup access to customers allowing them to scale and “go-global.”

• 55% Local Reach
  • 40% Scaleup Production
    • 55% ratio of startups with $1 billion+ valuations to GDP in H2 2021–2023
    • 45% ratio of $50 million+ exits to GDP in H2 2021–2023
      
  • 15% Local Market
    • 100% from the log of GDP of the country
      
• 45% Global Reach
  • 80% ratio of tech startups (formed after 2012) with international secondary offices
  • 20% from the log of tech companies with secondary offices in the ecosystem

Talent & Experience

Assesses the talent early-stage startups have access to and the degree of startup experience in an ecosystem.

• 43% Talent
  • 80% Tech Talent
    • 50% Quality & Access
      
      • 50% log of count of $50 million+ exits from 2014–2023
      • 30% share of top Github coders to total Github coders
      • 20% log of count of Github coders with more than 10 followers on github.com
    • 50% Cost
      • 50% log of software engineer salary — lower is considered better — from Glassdoor, Salary.com, and PayScale
      • 50% log of funding runway: the ratio of median Series A funding rounds by software engineer salary
  • 20% STEM Students: log of number of STEM students
• 57% Experience
  • 80% Startup Experience in Ecosystem
    • Log of count of Series A funding in 2014–2023
  • 20% Scaling Experience in Ecosystem (the cumulative number of significant $50 million+ and $1 billion+ exits over 10 years for startups founded in the ecosystem)
    • 60% log of the number of $1 billion+ exits in 2014–2023
    • 40% log of the number of $50 million+ exits in 2014–2023

Knowledge

Measures innovation through research and patent activity.

• 90% patents (the volume, complexity, and potential of all patents created in the ecosystem)
  • 60% log of tier of number of all the patents in the ecosystem in 2012–2022
  • 20% three-year moving average growth of all patents
  • 20% technology potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces
    • 20% complexity of technology class, based on a PageRank algorithm
    • 30% global growth of technology class
    • 50% size of technology class (log of number of global patents in class) in 2012–2022
      
• 10% research
  • H-index, a measure of publication impact. This metric looks at the production of all research at the country level.

Changes from GSER 2023

Startup Genome aims to continuously improve its data and research. Therefore, as with previous years, we have made a number of changes to our methodology to reflect our changing understanding, as well as our beliefs concerning the reliability and representativeness of data sources.

The most substantial change this year is the removal of the Connectedness Factor. This was previously composed of two sub-factors: local connectedness and global connectedness.

The local connectedness component was heavily dependent upon data concerning numbers of meetups, which we used as an indicator of relevant networks within a local ecosystem. However, it is our opinion that this indicator has become less useful since the pandemic, given an increase in virtual and hybrid events. Additionally, this component used data from Meetup.com, which, whilst being a global platform used in over 180 countries, has somewhat uneven adoption in less-developed ecosystems. Therefore, as we expand our coverage of ecosystems, we believe that usage data from Meetup.com will increasingly diverge from the underlying networks which we are attempting to measure. For these reasons, the local connectedness component was removed, and the global connectedness component merged back into the Market Reach category, as existed in some previous editions. This also necessitated a slight reweighting of the remaining categories.

Despite these changes, we continue to believe that networks are vitally important to entrepreneurship, and may reintroduce this category in the future as new data sources and methods for measuring entrepreneurial networks and human connections become available.

Another change concerned the use of patent data within the Knowledge category. Previous editions used the rate of growth of first-filings as a proxy indicator of entrepreneurially-relevant knowledge production. However, there were concerns that this measure artificially inflated the rank of ecosystems which subsidize patent filings, and might reward quantity over quality. We have therefore changed this measure slightly, from growth of first-filings to growth of PCT patent applications, which we consider a better indicator of both quality and quantity.

The 2025 edition of the GSER will include further changes. One will be a clearer description of sub-national ecosystems: in editions up to and including this one, we have referred to some smaller countries (such as Malta) or less populated countries (such as New Zealand) as whole ecosystems in themselves, so the rankings have contained a mix of cities and countries. However, since we intend to introduce a separate national entrepreneurship index, future editions of the GSER will refer to cities and their surrounding region only.

Changes in Ecosystem Value

It is our constant endeavor to improve our quality of research and data in order to help our members and readers gain accurate and current knowledge on global startup ecosystems. With that aim in mind, we have significantly improved our data set since the GSER 2021 — both in terms of exhaustiveness and quality. As we improved the data, one of the key outcomes was an increase in Ecosystem Value. The major factors that influenced this are:

• Technology startup classification: we have made significant improvements in our classification of technology companies by adding more comprehensive classification criteria and tags from multiple sources. We have added CB Insights data and introduced in-depth checks to ensure the tech classification is accurate. This resulted in more companies being tagged as tech and hence more deals added to our dataset. This contributed approximately 8% to Ecosystem Value.
• Increasing the age criteria: we concluded that older startups are more likely to receive higher and late-stage funding rounds. With that in mind, for exits over $100 million we included companies with formation dates that go back to 1995. For rounds later than Series B, we also include companies with formation dates since 1995 in our dataset.
• Increasing unicorns data: we have made enormous strides in expanding unicorn coverage in our dataset. This includes incorporating CB Insights unicorns and $1 billion+ exits (after in-depth checks). This contributed to an approximate 36% increase in Ecosystem Value of the top ecosystems.
• Fine-combing through big deals: as a final check, we scrupulously examined the larger deals of each ecosystem to make sure that every deal was valid, reflected the true value, and belonged to that particular ecosystem.
• From last year, we are including exits larger than $500 million that took place after H2 2019. These large exits stay in their ecosystem, mostly in the form of dry powder for investors to expand their portfolios.
• In previous years, we have only considered the ecosystem that a startup is founded in. From last year, we have also added the value of the top five startups and/or unicorns to the ecosystem where the startup is headquartered. The intention is to attribute both where a startup is born and where it creates attraction.